计算机科学
一般化
人工智能
蛋白质-蛋白质相互作用
学习迁移
水准点(测量)
变压器
机器学习
编码器
计算生物学
生物
遗传学
数学
数学分析
物理
大地测量学
量子力学
电压
地理
操作系统
作者
Tuoyu Liu,Han Gao,Xiaomin Ren,Guanglong Xu,Bo Liu,Ningfeng Wu,Huiying Luo,Yuan Wang,Tao Tu,Bin Yao,Feifei Guan,Yanguo Teng,Huoqing Huang,Jian Tian
摘要
Abstract The advanced language models have enabled us to recognize protein–protein interactions (PPIs) and interaction sites using protein sequences or structures. Here, we trained the MindSpore ProteinBERT (MP-BERT) model, a Bidirectional Encoder Representation from Transformers, using protein pairs as inputs, making it suitable for identifying PPIs and their respective interaction sites. The pretrained model (MP-BERT) was fine-tuned as MPB-PPI (MP-BERT on PPI) and demonstrated its superiority over the state-of-the-art models on diverse benchmark datasets for predicting PPIs. Moreover, the model’s capability to recognize PPIs among various organisms was evaluated on multiple organisms. An amalgamated organism model was designed, exhibiting a high level of generalization across the majority of organisms and attaining an accuracy of 92.65%. The model was also customized to predict interaction site propensity by fine-tuning it with PPI site data as MPB-PPISP. Our method facilitates the prediction of both PPIs and their interaction sites, thereby illustrating the potency of transfer learning in dealing with the protein pair task.
科研通智能强力驱动
Strongly Powered by AbleSci AI